Overview

Dataset statistics

 TrainTest
Number of variables66
Number of observations36856000
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory172.9 KiB281.4 KiB
Average record size in memory48.0 B48.0 B

Variable types

 TrainTest
Numeric66

Alerts

TrainTest
Adj_Close is highly overall correlated with Close and 4 other fieldsAdj_Close is highly overall correlated with Close and 4 other fieldsHigh Correlation
Close is highly overall correlated with Adj_Close and 4 other fieldsClose is highly overall correlated with Adj_Close and 4 other fieldsHigh Correlation
High is highly overall correlated with Adj_Close and 4 other fieldsHigh is highly overall correlated with Adj_Close and 4 other fieldsHigh Correlation
Low is highly overall correlated with Adj_Close and 4 other fieldsLow is highly overall correlated with Adj_Close and 4 other fieldsHigh Correlation
Open is highly overall correlated with Adj_Close and 4 other fieldsOpen is highly overall correlated with Adj_Close and 4 other fieldsHigh Correlation
Volume is highly overall correlated with Adj_Close and 4 other fieldsVolume is highly overall correlated with Adj_Close and 4 other fieldsHigh Correlation
Alert not present in this datasetLow has unique values Unique

Reproduction

 TrainTest
Analysis started2024-06-26 15:39:25.6821372024-06-26 15:39:33.488544
Analysis finished2024-06-26 15:39:33.4855552024-06-26 15:39:40.206817
Duration7.8 seconds6.72 seconds
Software versionydata-profiling v4.8.3ydata-profiling v4.8.3
Download configurationconfig.jsonconfig.json

Variables

Open
Real number (ℝ)

 TrainTest
Distinct35725950
Distinct (%)96.9%99.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean453.22737468.92089
 TrainTest
Minimum49.27451749.274517
Maximum12711271
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size28.9 KiB47.0 KiB
2024-06-27T00:39:50.444305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum49.27451749.274517
5-th percentile113.89919140.93804
Q1233.24794234.80712
median306.94861335.78412
Q3621.21997596.87826
95-th percentile1097.071147.1864
Maximum12711271
Range1221.72551221.7255
Interquartile range (IQR)387.97203362.07114

Descriptive statistics

 TrainTest
Standard deviation305.01869308.7953
Coefficient of variation (CV)0.672992660.65852324
Kurtosis-0.167979110.13563926
Mean453.22737468.92089
Median Absolute Deviation (MAD)132.92047171.04331
Skewness0.980904451.038903
Sum1670142.92813525.3
Variance93036.40295354.538
MonotonicityNot monotonicNot monotonic
2024-06-27T00:39:50.774833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
980 4
 
0.1%
293.590485 4
 
0.1%
228.513733 3
 
0.1%
261.558807 3
 
0.1%
780 3
 
0.1%
279.680939 3
 
0.1%
305.512939 3
 
0.1%
289.616333 2
 
0.1%
1088 2
 
0.1%
298.334625 2
 
0.1%
Other values (3562) 3656
99.2%
ValueCountFrequency (%)
1271 51
 
0.9%
751.2725966 1
 
< 0.1%
975.1939086 1
 
< 0.1%
540.2672509 1
 
< 0.1%
247.2255649 1
 
< 0.1%
128.9860582 1
 
< 0.1%
1086.657816 1
 
< 0.1%
197.23911 1
 
< 0.1%
205.1805933 1
 
< 0.1%
149.1562543 1
 
< 0.1%
Other values (5940) 5940
99.0%
ValueCountFrequency (%)
49.274517 1
< 0.1%
49.676899 1
< 0.1%
50.04451 1
< 0.1%
50.14883 1
< 0.1%
50.178635 2
0.1%
50.471729 1
< 0.1%
50.819469 1
< 0.1%
50.933723 1
< 0.1%
51.018177 1
< 0.1%
52.135906 1
< 0.1%
ValueCountFrequency (%)
49.274517 1
< 0.1%
51.34243656 1
< 0.1%
51.44532223 1
< 0.1%
52.05508998 1
< 0.1%
53.07126684 1
< 0.1%
55.0348228 1
< 0.1%
55.79117984 1
< 0.1%
56.5290987 1
< 0.1%
57.1548786 1
< 0.1%
57.40011113 1
< 0.1%
ValueCountFrequency (%)
49.274517 1
< 0.1%
51.34243656 1
< 0.1%
51.44532223 1
< 0.1%
52.05508998 1
< 0.1%
53.07126684 1
< 0.1%
55.0348228 1
< 0.1%
55.79117984 1
< 0.1%
56.5290987 1
< 0.1%
57.1548786 1
< 0.1%
57.40011113 1
< 0.1%
ValueCountFrequency (%)
49.274517 1
< 0.1%
49.676899 1
< 0.1%
50.04451 1
< 0.1%
50.14883 1
< 0.1%
50.178635 2
< 0.1%
50.471729 1
< 0.1%
50.819469 1
< 0.1%
50.933723 1
< 0.1%
51.018177 1
< 0.1%
52.135906 1
< 0.1%

High
Real number (ℝ)

 TrainTest
Distinct35585978
Distinct (%)96.6%99.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean457.33135474.0734
 TrainTest
Minimum50.54127951.772301
Maximum1273.891273.89
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size28.9 KiB47.0 KiB
2024-06-27T00:39:51.099622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum50.54127951.772301
5-th percentile115.0974157.25106
Q1235.39896233.92547
median309.35297341.5386
Q3627.54999577.6555
95-th percentile1106.5291159.47
Maximum1273.891273.89
Range1223.34871222.1177
Interquartile range (IQR)392.15103343.73004

Descriptive statistics

 TrainTest
Standard deviation307.4493314.93103
Coefficient of variation (CV)0.672268150.66430858
Kurtosis-0.158204970.15078599
Mean457.33135474.0734
Median Absolute Deviation (MAD)132.48828164.84166
Skewness0.985720071.1053991
Sum16852662844440.4
Variance94525.07399181.554
MonotonicityNot monotonicNot monotonic
2024-06-27T00:39:51.429843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234.971741 3
 
0.1%
1200 3
 
0.1%
252.855423 3
 
0.1%
251.861877 3
 
0.1%
258.319885 3
 
0.1%
309.983856 3
 
0.1%
240.932968 3
 
0.1%
201.688217 3
 
0.1%
261.707855 3
 
0.1%
193.739914 3
 
0.1%
Other values (3548) 3655
99.2%
ValueCountFrequency (%)
1273.890015 23
 
0.4%
588.817911 1
 
< 0.1%
836.676059 1
 
< 0.1%
1134.765424 1
 
< 0.1%
201.1162149 1
 
< 0.1%
1264.780816 1
 
< 0.1%
143.5473255 1
 
< 0.1%
262.894712 1
 
< 0.1%
236.4886646 1
 
< 0.1%
225.9955143 1
 
< 0.1%
Other values (5968) 5968
99.5%
ValueCountFrequency (%)
50.541279 1
< 0.1%
50.670437 1
< 0.1%
50.85424 1
< 0.1%
51.023144 1
< 0.1%
51.152302 1
< 0.1%
51.18211 1
< 0.1%
51.519913 1
< 0.1%
51.693783 1
< 0.1%
52.40416 1
< 0.1%
52.935703 1
< 0.1%
ValueCountFrequency (%)
51.77230066 1
< 0.1%
52.24955897 1
< 0.1%
53.6522599 1
< 0.1%
53.87885375 1
< 0.1%
57.90173509 1
< 0.1%
58.48716426 1
< 0.1%
58.63561408 1
< 0.1%
58.6420821 1
< 0.1%
58.95197197 1
< 0.1%
60.36271074 1
< 0.1%
ValueCountFrequency (%)
51.77230066 1
< 0.1%
52.24955897 1
< 0.1%
53.6522599 1
< 0.1%
53.87885375 1
< 0.1%
57.90173509 1
< 0.1%
58.48716426 1
< 0.1%
58.63561408 1
< 0.1%
58.6420821 1
< 0.1%
58.95197197 1
< 0.1%
60.36271074 1
< 0.1%
ValueCountFrequency (%)
50.541279 1
< 0.1%
50.670437 1
< 0.1%
50.85424 1
< 0.1%
51.023144 1
< 0.1%
51.152302 1
< 0.1%
51.18211 1
< 0.1%
51.519913 1
< 0.1%
51.693783 1
< 0.1%
52.40416 1
< 0.1%
52.935703 1
< 0.1%

Low
Real number (ℝ)

 TrainTest
Distinct35866000
Distinct (%)97.3%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean448.81182480.54177
 TrainTest
Minimum47.66995249.205056
Maximum1249.021244.3373
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size28.9 KiB47.0 KiB
2024-06-27T00:39:51.749874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum47.66995249.205056
5-th percentile113.25538138.42657
Q1230.75417240.60896
median304.51443350.93181
Q3612.40002596.87435
95-th percentile1086.32161148.985
Maximum1249.021244.3373
Range1201.35011195.1322
Interquartile range (IQR)381.64586356.2654

Descriptive statistics

 TrainTest
Standard deviation302.54917315.06922
Coefficient of variation (CV)0.674111410.65565417
Kurtosis-0.1754453-0.16536413
Mean448.81182480.54177
Median Absolute Deviation (MAD)133.33777176.87089
Skewness0.977820580.96599609
Sum1653871.52883250.6
Variance91535.99899268.612
MonotonicityNot monotonicNot monotonic
2024-06-27T00:39:52.073129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
287.132477 3
 
0.1%
237.703964 3
 
0.1%
257.82312 3
 
0.1%
253.352188 3
 
0.1%
541.07489 2
 
0.1%
149.924881 2
 
0.1%
372.700928 2
 
0.1%
293.193054 2
 
0.1%
300.048462 2
 
0.1%
262.045654 2
 
0.1%
Other values (3576) 3661
99.3%
ValueCountFrequency (%)
1020.525608 1
 
< 0.1%
247.2428919 1
 
< 0.1%
1151.53746 1
 
< 0.1%
564.1636376 1
 
< 0.1%
198.4466715 1
 
< 0.1%
354.2736153 1
 
< 0.1%
227.3432494 1
 
< 0.1%
523.8975262 1
 
< 0.1%
293.7213668 1
 
< 0.1%
610.4043928 1
 
< 0.1%
Other values (5990) 5990
99.8%
ValueCountFrequency (%)
47.669952 1
< 0.1%
49.150326 1
< 0.1%
49.339096 1
< 0.1%
49.483158 1
< 0.1%
49.512966 1
< 0.1%
49.925285 2
0.1%
50.173668 1
< 0.1%
50.322701 1
< 0.1%
50.675404 1
< 0.1%
50.74992 1
< 0.1%
ValueCountFrequency (%)
49.20505554 1
< 0.1%
49.70768714 1
< 0.1%
49.85203357 1
< 0.1%
50.47931017 1
< 0.1%
50.75373468 1
< 0.1%
51.39498513 1
< 0.1%
51.5652876 1
< 0.1%
53.39089999 1
< 0.1%
55.34687533 1
< 0.1%
55.488944 1
< 0.1%
ValueCountFrequency (%)
49.20505554 1
< 0.1%
49.70768714 1
< 0.1%
49.85203357 1
< 0.1%
50.47931017 1
< 0.1%
50.75373468 1
< 0.1%
51.39498513 1
< 0.1%
51.5652876 1
< 0.1%
53.39089999 1
< 0.1%
55.34687533 1
< 0.1%
55.488944 1
< 0.1%
ValueCountFrequency (%)
47.669952 1
< 0.1%
49.150326 1
< 0.1%
49.339096 1
< 0.1%
49.483158 1
< 0.1%
49.512966 1
< 0.1%
49.925285 2
< 0.1%
50.173668 1
< 0.1%
50.322701 1
< 0.1%
50.675404 1
< 0.1%
50.74992 1
< 0.1%

Close
Real number (ℝ)

 TrainTest
Distinct36115984
Distinct (%)98.0%99.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean453.15492458.87706
 TrainTest
Minimum49.68186650.208599
Maximum1268.331268.33
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size28.9 KiB47.0 KiB
2024-06-27T00:39:52.587104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum49.68186650.208599
5-th percentile114.05916145.77183
Q1233.43672238.73093
median306.44189338.52938
Q3622.69577.53793
95-th percentile1096.2821114.7359
Maximum1268.331268.33
Range1218.64811218.1214
Interquartile range (IQR)389.25328338.80701

Descriptive statistics

 TrainTest
Standard deviation305.12944292.62903
Coefficient of variation (CV)0.673344640.63770681
Kurtosis-0.16638820.31698397
Mean453.15492458.87706
Median Absolute Deviation (MAD)133.24338173.72002
Skewness0.981984891.0791325
Sum1669875.92753262.4
Variance93103.97485631.747
MonotonicityNot monotonicNot monotonic
2024-06-27T00:39:52.921906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288.126007 3
 
0.1%
306.258087 3
 
0.1%
289.616333 3
 
0.1%
220.083572 2
 
0.1%
273.769409 2
 
0.1%
241.931473 2
 
0.1%
296.282959 2
 
0.1%
786.900024 2
 
0.1%
191.727997 2
 
0.1%
170.391769 2
 
0.1%
Other values (3601) 3662
99.4%
ValueCountFrequency (%)
1268.329956 17
 
0.3%
1176.07947 1
 
< 0.1%
590.6741959 1
 
< 0.1%
287.4961389 1
 
< 0.1%
541.9153955 1
 
< 0.1%
764.7777522 1
 
< 0.1%
1129.829726 1
 
< 0.1%
539.1507705 1
 
< 0.1%
527.4054176 1
 
< 0.1%
261.924488 1
 
< 0.1%
Other values (5974) 5974
99.6%
ValueCountFrequency (%)
49.681866 1
< 0.1%
49.80109 1
< 0.1%
49.845802 1
< 0.1%
50.427021 1
< 0.1%
50.461796 1
< 0.1%
50.675404 1
< 0.1%
50.819469 1
< 0.1%
50.824436 1
< 0.1%
50.85424 1
< 0.1%
52.096165 1
< 0.1%
ValueCountFrequency (%)
50.20859926 1
< 0.1%
51.40266131 1
< 0.1%
52.75285735 1
< 0.1%
55.1556096 1
< 0.1%
58.43589652 1
< 0.1%
61.90018681 1
< 0.1%
64.56996195 1
< 0.1%
65.51767198 1
< 0.1%
66.10793205 1
< 0.1%
66.94779223 1
< 0.1%
ValueCountFrequency (%)
50.20859926 1
< 0.1%
51.40266131 1
< 0.1%
52.75285735 1
< 0.1%
55.1556096 1
< 0.1%
58.43589652 1
< 0.1%
61.90018681 1
< 0.1%
64.56996195 1
< 0.1%
65.51767198 1
< 0.1%
66.10793205 1
< 0.1%
66.94779223 1
< 0.1%
ValueCountFrequency (%)
49.681866 1
< 0.1%
49.80109 1
< 0.1%
49.845802 1
< 0.1%
50.427021 1
< 0.1%
50.461796 1
< 0.1%
50.675404 1
< 0.1%
50.819469 1
< 0.1%
50.824436 1
< 0.1%
50.85424 1
< 0.1%
52.096165 1
< 0.1%

Adj_Close
Real number (ℝ)

 TrainTest
Distinct36115983
Distinct (%)98.0%99.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean453.15492472.37242
 TrainTest
Minimum49.68186649.681866
Maximum1268.331268.33
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size28.9 KiB47.0 KiB
2024-06-27T00:39:53.236361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum49.68186649.681866
5-th percentile114.05916139.87663
Q1233.43672234.72919
median306.44189341.19866
Q3622.69581.06927
95-th percentile1096.2821126.0082
Maximum1268.331268.33
Range1218.64811218.6481
Interquartile range (IQR)389.25328346.34008

Descriptive statistics

 TrainTest
Standard deviation305.12944312.97386
Coefficient of variation (CV)0.673344640.66255744
Kurtosis-0.1663882-0.089876472
Mean453.15492472.37242
Median Absolute Deviation (MAD)133.24338177.27028
Skewness0.981984890.99972466
Sum1669875.92834234.5
Variance93103.97497952.637
MonotonicityNot monotonicNot monotonic
2024-06-27T00:39:53.561712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288.126007 3
 
0.1%
306.258087 3
 
0.1%
289.616333 3
 
0.1%
220.083572 2
 
0.1%
273.769409 2
 
0.1%
241.931473 2
 
0.1%
296.282959 2
 
0.1%
786.900024 2
 
0.1%
191.727997 2
 
0.1%
170.391769 2
 
0.1%
Other values (3601) 3662
99.4%
ValueCountFrequency (%)
1268.329956 18
 
0.3%
526.1363072 1
 
< 0.1%
240.8659051 1
 
< 0.1%
405.9325905 1
 
< 0.1%
949.5457019 1
 
< 0.1%
973.8616363 1
 
< 0.1%
570.5161612 1
 
< 0.1%
524.7510312 1
 
< 0.1%
202.6933348 1
 
< 0.1%
226.8473937 1
 
< 0.1%
Other values (5973) 5973
99.6%
ValueCountFrequency (%)
49.681866 1
< 0.1%
49.80109 1
< 0.1%
49.845802 1
< 0.1%
50.427021 1
< 0.1%
50.461796 1
< 0.1%
50.675404 1
< 0.1%
50.819469 1
< 0.1%
50.824436 1
< 0.1%
50.85424 1
< 0.1%
52.096165 1
< 0.1%
ValueCountFrequency (%)
49.681866 1
< 0.1%
50.60709307 1
< 0.1%
51.09476939 1
< 0.1%
52.46077886 1
< 0.1%
54.87260769 1
< 0.1%
54.96956844 1
< 0.1%
55.07413087 1
< 0.1%
55.10250232 1
< 0.1%
55.69594076 1
< 0.1%
57.2914406 1
< 0.1%
ValueCountFrequency (%)
49.681866 1
< 0.1%
50.60709307 1
< 0.1%
51.09476939 1
< 0.1%
52.46077886 1
< 0.1%
54.87260769 1
< 0.1%
54.96956844 1
< 0.1%
55.07413087 1
< 0.1%
55.10250232 1
< 0.1%
55.69594076 1
< 0.1%
57.2914406 1
< 0.1%
ValueCountFrequency (%)
49.681866 1
< 0.1%
49.80109 1
< 0.1%
49.845802 1
< 0.1%
50.427021 1
< 0.1%
50.461796 1
< 0.1%
50.675404 1
< 0.1%
50.819469 1
< 0.1%
50.824436 1
< 0.1%
50.85424 1
< 0.1%
52.096165 1
< 0.1%

Volume
Real number (ℝ)

 TrainTest
Distinct36145998
Distinct (%)98.1%> 99.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean7391935.87448476.5
 TrainTest
Minimum7900686774
Maximum8276810063251283
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size28.9 KiB47.0 KiB
2024-06-27T00:39:53.883015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum7900686774
5-th percentile10992201106933.9
Q119592001686190.5
median46745004920069.5
Q3972390011446293
95-th percentile2258004019646520
Maximum8276810063251283
Range8276020062564509
Interquartile range (IQR)77647009760102.2

Descriptive statistics

 TrainTest
Standard deviation8197565.16961497.1
Coefficient of variation (CV)1.10898760.93462027
Kurtosis14.3515795.5148884
Mean7391935.87448476.5
Median Absolute Deviation (MAD)30648003558125.5
Skewness3.00079971.8479681
Sum2.7239283 × 10104.4690859 × 1010
Variance6.7200074 × 10134.8462442 × 1013
MonotonicityNot monotonicNot monotonic
2024-06-27T00:39:54.194519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1389600 3
 
0.1%
1290800 3
 
0.1%
1173900 2
 
0.1%
1849800 2
 
0.1%
1372500 2
 
0.1%
9752900 2
 
0.1%
4261500 2
 
0.1%
15738400 2
 
0.1%
3506500 2
 
0.1%
1565900 2
 
0.1%
Other values (3604) 3663
99.4%
ValueCountFrequency (%)
3831625 2
 
< 0.1%
1706126 2
 
< 0.1%
8765287 1
 
< 0.1%
1265115 1
 
< 0.1%
5113765 1
 
< 0.1%
5879836 1
 
< 0.1%
55990679 1
 
< 0.1%
6524930 1
 
< 0.1%
1639628 1
 
< 0.1%
7253916 1
 
< 0.1%
Other values (5988) 5988
99.8%
ValueCountFrequency (%)
7900 1
< 0.1%
10800 1
< 0.1%
13100 1
< 0.1%
41300 1
< 0.1%
147500 1
< 0.1%
527200 1
< 0.1%
537000 1
< 0.1%
587400 1
< 0.1%
623400 1
< 0.1%
679000 1
< 0.1%
ValueCountFrequency (%)
686774 1
< 0.1%
702316 1
< 0.1%
735526 1
< 0.1%
769299 1
< 0.1%
770286 1
< 0.1%
777232 1
< 0.1%
779882 1
< 0.1%
783803 1
< 0.1%
789712 1
< 0.1%
804488 1
< 0.1%
ValueCountFrequency (%)
686774 1
< 0.1%
702316 1
< 0.1%
735526 1
< 0.1%
769299 1
< 0.1%
770286 1
< 0.1%
777232 1
< 0.1%
779882 1
< 0.1%
783803 1
< 0.1%
789712 1
< 0.1%
804488 1
< 0.1%
ValueCountFrequency (%)
7900 1
< 0.1%
10800 1
< 0.1%
13100 1
< 0.1%
41300 1
< 0.1%
147500 1
< 0.1%
527200 1
< 0.1%
537000 1
< 0.1%
587400 1
< 0.1%
623400 1
< 0.1%
679000 1
< 0.1%

Interactions

Train

2024-06-27T00:39:31.766422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:38.730026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:26.490798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:33.679154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:27.540356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:34.678180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:28.674620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:35.666928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:29.677657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:36.720342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:30.776397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:37.729549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:31.932083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:39.030432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:26.698300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:33.844094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:27.709441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:34.844494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:28.842439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:35.891181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:29.842129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:36.887799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:30.941727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:37.894929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:32.091048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:39.195883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:26.899610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:34.009883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:27.877155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:35.002861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:29.003770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:36.054260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:30.028135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:37.052622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:31.106178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:38.064527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:32.255084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:39.358871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:27.062257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:34.177119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:28.092206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:35.174554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:29.169729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:36.217631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:30.243575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:37.216196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:31.277355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:38.231074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:32.416157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:39.532114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:27.226331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:34.344987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:28.280977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:35.339052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:29.331251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:36.386479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:30.428232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:37.387573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:31.442727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:38.398763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:32.577497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:39.696926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:27.389073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:34.511667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:28.492618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:35.501887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:29.518444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:36.551369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:30.614590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:37.551295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

2024-06-27T00:39:31.610049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:38.563187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

Train

2024-06-27T00:39:54.364112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Test

2024-06-27T00:39:54.555365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Train

Adj_CloseCloseHighLowOpenVolume
Adj_Close1.0001.0001.0001.0001.000-0.860
Close1.0001.0001.0001.0001.000-0.860
High1.0001.0001.0001.0001.000-0.857
Low1.0001.0001.0001.0001.000-0.862
Open1.0001.0001.0001.0001.000-0.859
Volume-0.860-0.860-0.857-0.862-0.8591.000

Test

Adj_CloseCloseHighLowOpenVolume
Adj_Close1.0000.6680.6600.6690.675-0.621
Close0.6681.0000.6710.6690.673-0.634
High0.6600.6711.0000.6680.672-0.629
Low0.6690.6690.6681.0000.686-0.637
Open0.6750.6730.6720.6861.000-0.639
Volume-0.621-0.634-0.629-0.637-0.6391.000

Missing values

Train

2024-06-27T00:39:32.899737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

Test

2024-06-27T00:39:39.913611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

Train

2024-06-27T00:39:33.097393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Test

2024-06-27T00:39:40.111005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Train

OpenHighLowCloseAdj_CloseVolume
049.67689951.69378347.66995249.84580249.84580244994500
150.17863554.18756149.92528553.80505053.80505023005800
255.01716656.37334454.17266154.34652754.34652718393200
355.26058255.43941951.45036352.09616552.09616515361800
452.14087353.65105151.60436252.65751352.6575139257400
552.13590653.62621351.99184453.60634253.6063427148200
653.70072953.95904952.50351352.73202952.7320296258300
752.29983952.40416050.67540450.67540450.6754045235700
850.81946951.51991350.74992050.85424050.8542404954800
951.01817751.15230249.51296649.80109049.8010909206800

Test

OpenHighLowCloseAdj_CloseVolume
0751.272597588.8179111020.5256081176.0794701268.3299561461343
11220.9320231142.248973976.645249564.969605780.0423731573974
2513.883402606.970192921.403243553.66284570.5496284584897
3307.542602291.515053291.841689336.567462300.0432057598497
41240.8217461272.938204557.251988603.2108101163.9988311309691
5283.770548244.996657174.753140327.637288330.8760225894606
6722.473300528.172557142.294865183.08890593.9563566143949
7326.051359330.868832309.643267334.982638333.3745008762774
8207.402336268.590509285.047515273.246719208.57468616440255
9218.482585170.288397133.735418319.084682166.12297610109980

Train

OpenHighLowCloseAdj_CloseVolume
36751185.5000001187.5589601159.3699951173.0200201173.0200201400200
36761171.5400391171.5649411159.4310301168.4899901168.4899901012400
36771174.9000241178.9899901162.8800051173.3100591173.3100591269900
36781184.0999761196.6600341182.0000001194.4300541194.4300541252500
36791195.3199461201.3499761185.7099611200.4899901200.489990827900
36801207.4799801216.3000491200.5000001205.9200441205.9200441017800
36811205.9399411215.6700441204.1300051215.0000001215.000000950000
36821214.9899901216.2199711205.0300291207.1500241207.150024907200
36831207.8900151208.6899411199.8599851203.8399661203.839966860200
36841196.0000001202.2900391193.0799561197.2500001197.250000865500

Test

OpenHighLowCloseAdj_CloseVolume
5990430.4048451079.5059191163.756160304.8416921029.4817344395577
5991286.034495225.689732226.309919239.363902176.20700814671620
5992165.914694144.143448221.061744189.808097175.46249911985850
5993520.523515535.731345521.716358190.641400604.3511726977739
5994300.093876339.401847285.618627264.094756284.5822999957898
5995227.386309565.268126216.274740450.975436249.26358814808867
5996583.9506681112.459258113.971465803.575205567.0181891285039
5997251.101808564.800129540.224776116.07484261.39911711606614
5998252.495769215.09149151.394985365.139969555.3411458305337
59991150.6529511093.5392241173.4720361059.5739481144.4694961797192

Duplicate rows

Train

OpenHighLowCloseAdj_CloseVolume# duplicates
Dataset does not contain duplicate rows.

Test

OpenHighLowCloseAdj_CloseVolume# duplicates
Dataset does not contain duplicate rows.